While causal discovery using medical images is limited to some applications involving f-MRI scans, causal inference is significantly more active as an area of research. Highlighting its importance, [1]} argue that causal inference can be used to alleviate some of the most prominent problems in medical imaging. They argue that acquisition and annotation of medical images can exhibit bias from the annotators and curators of the datasets. As such, causality aware methods can learn to account for such biases and reduce their effects. Moreover, as the training datasets represent a limited population with specific characteristics, medical imaging algorithms are susceptible to population, selection and prevalence biases if not properly controlled for these variations. These biases could for example arise when an algorithm is trained by a vast majority on data that come from a given geographical region \(X\) , then it implicitly learns the prevalence of diseases for that group; if it is deployed in a different region with a population that is characterized by different genotype and phenotype characteristics, the biases that the algorithm has learned could lead to mis- or under- diagnosis of diseases.
